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Record W2159805234 · doi:10.1177/0093854811404120

Fear and Loathing in Psychopaths: a Meta-Analytic Investigation of the Facial Affect Recognition Deficit

2011· article· en· W2159805234 on OpenAlexaff
Kevin Wilson, Marcus Juodis, Stephen Porter

Bibliographic record

VenueCriminal Justice and Behavior · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of British ColumbiaDalhousie University
Fundersnot available
KeywordsPsychopathyPsychologyAffect (linguistics)Facial expressionCognitive psychologyAmygdalaEmotion recognitionPopulationMeta-analysisDevelopmental psychologyPersonalitySocial psychologyNeuroscienceMedicineCommunication

Abstract

fetched live from OpenAlex

Several studies have identified an association between psychopathy and deficits in facial affect recognition. Although this finding is widely seen as providing strong evidence for amygdala dysfunction in psychopaths, this interpretation is challenged by studies finding no recognition impairments. An alternative hypothesis predicts that recognition deficits are dynamic and are influenced by verbal processing demands. These competing hypotheses were tested via a meta-analysis of 22 investigations of psychopathy ( N = 1,387 participants) using the facial affect recognition paradigm. Results indicated that studies entailing a verbal response style found larger recognition deficits for emotions processed by the left amygdala. The findings of this review offer an alternative to currently popular theories of psychopathy and suggest that future research should consider response style when investigating facial affect recognition deficits in this population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.011
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.216
GPT teacher head0.350
Teacher spread0.134 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations106
Published2011
Admission routes1
Has abstractyes

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